Synthesis, Photophysical Properties, Theoretical Studies, and Living Cancer Cell Imaging Applications of New 7-(Diethylamino)quinolone Chalcones.

In this article, three unsymmetrical 7-(diethylamino)quinolone chalcones with D-π-A-D and D-π-A-π-D type push-pull molecular arrangements were synthesized via a Claisen-Schmidt reaction. Using 7-(diethylamino)quinolone and vanillin as electron donor (D) moieties, these were linked together through the α,ß-unsaturated carbonyl system acting as a linker and an electron acceptor (A). The photophysical properties were studied, revealing significant Stokes shifts and strong solvatofluorochromism caused by the ICT and TICT behavior produced by the push-pull effect. Moreover, quenching caused by the population of the TICT state in THF-H2O mixtures was observed, and the emission in the solid state evidenced a red shift compared to the emission in solution. These findings were corroborated by density functional theory (DFT) calculations employing the wb97xd/6-311G(d,p) method. The cytotoxic activity of the synthesized compounds was assessed on BHK-21, PC3, and LNCaP cell lines, revealing moderate activity across all compounds. Notably, compound 5b exhibited the highest activity against LNCaP cells, with an LC50 value of 10.89 µM. Furthermore, the compounds were evaluated for their potential as imaging agents in living prostate cells. The results demonstrated their favorable cell permeability and strong emission at 488 nm, positioning them as promising candidates for cancer cell imaging applications.

Photoelectrocatalytic degradation of high-density polyethylene microplastics on TiO2-modified boron-doped diamond photoanode.

Microplastic (MP) accumulation in the environment is accelerating rapidly, which has led to their effects on both the ecosystem and human life garnering much attention. This study is the first to examine the degradation of high-density polyethylene (HDPE) MPs via photoelectrocatalysis (PEC) using a TiO2-modified boron-doped diamond (BDD/TiO2) photoanode. This study was divided into three stages: (i) preparation of the photoanode through electrophoretic deposition of synthetic TiO2 nanoparticles on a BDD electrode; (ii) characterization of the modified photoanode using electrochemical, structural, and optical techniques; and (iii) degradation of HDPE MPs by electrochemical oxidation and photoelectrocatalysis on bare and modified BDD electrodes under dark and UV light conditions. The results indicate that the PEC technique degraded 89.91 ± 0.08% of HDPE MPs in a 10-h reaction and was more efficient at a lower current density (6.89 mA cm-1) with the BDD/TiO2 photoanode compared to electrochemical oxidation on bare BDD.

Pharmaceuticals, hazard and ecotoxicity in surface and wastewater in a tropical dairy production area in Latin America.

Pharmaceuticals comprise a complex group of emerging pollutants. Despite the significant number of pharmaceuticals used in veterinary medicine, the input of these compounds into the environment due to livestock activities has been scarcely described. This work assays for the first time in Central America the occurrence of pharmaceuticals in farm wastewater in an area devoted to dairy production, and in the surrounding surface waters. Among 69 monitored pharmaceuticals, a total of eight compounds were detected in wastewater samples collected from seven dairy farms after three sampling campaigns. Six pharmaceuticals were considered either of high (albendazole, lovastatin and caffeine) or intermediate estimated hazard (ciprofloxacin, acetaminophen and ketoprofen) based on the HQ approach, while 26% of the samples were considered of high estimated hazard according to the cumulative ∑HQ approach. Similarly, when ecotoxicological tests were applied, all the samples showed some level of toxicity towards Daphnia magna, and most samples towards Vibrio fischeri and Lactuca sativa. Fourteen pharmaceuticals were detected in surface water samples collected in the surroundings of the dairy production farms, including rural and urban areas. Seven out of these compounds showed high estimated risk (risperidone, diphenhydramine, trimethoprim, fluoxetine, ofloxacin, caffeine and ibuprofen), while three (gemfibrozil, ciprofloxacin and cephalexin) exhibited intermediate estimated risk. In a similar worrisome way, 27% of these samples were estimated to pose high environmental risk according to the pharmaceutical content. Despite being nontoxic for D. magna or V. fischeri, frequent inhibition (>20%) of GI in L. sativa was determined in 34% of surface water samples; such findings raise concern on the apparent inceptive environmental pollution and risk within the area. According to the pharmaceutical content patterns in both kinds of studied matrices, no clear evidence of significant contamination in surface water due to livestock activities could be retrieved, suggesting a main role of urban influence.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

On-farm Occurrence of Pharmaceuticals and Their Environmental Hazard: Case Study of a Tropical Dairy farm.

Animal husbandry wastewaters represent an important source of pharmaceuticals into the environment. This work aimed to evaluate the occurrence of pharmaceuticals and their hazard in wastewater from a model dairy farm from Costa Rica. Among the seven pharmaceuticals detected (acetaminophen, caffeine, carbamazepine, ibuprofen, ketoprofen, risperidone, sulfamethazine), caffeine, ibuprofen and acetaminophen showed the highest concentrations, while caffeine, carbamazepine and risperidone were the most frequently detected compounds. High (HQ ≥ 1) or medium (0.1 ≤ HQ < 1) hazard were estimated for three (caffeine, ibuprofen, risperidone) and two (acetaminophen, ketoprofen) pharmaceuticals, respectively; similarly, high overall hazard (∑HQ) and significant ecotoxicity were determined in samples from all sampling points. According to our results, the release of these aqueous matrices is a matter of environmental concern, as the treated wastewater is used for farm irrigation or directly released into nearby water streams. This work contributes to the knowledge on the scarcely described occurrence and risk of pharmaceuticals in Latin American regions.

Occurrence and risk assessment of pharmaceuticals in hospital wastewater in Costa Rica.

This work aims to determine the occurrence, hazard and prioritization of pharmaceuticals from hospital wastewater in Costa Rica through the monitoring of 70 compounds and assessing their environmental risk through a hazard quotient approach (HQ). Moreover, the quantification of selected antibiotic resistance genes (ARGs) was conducted for the first time in this matrix in this geographical location. Thirty-four pharmaceuticals were detected, being caffeine, 1,7-dimethylxanthine, acetaminophen, ibuprofen, naproxen, ciprofloxacin and ketoprofen the most frequent (>50% of the samples). Eighteen pharmaceuticals exhibited high hazard (HQ ≥ 1), while five more showed medium hazard (1 > HQ ≥ 0.1). Prioritization, which also included frequency parameters, revealed caffeine, lovastatin, diphenhydramine, acetaminophen, ibuprofen, ciprofloxacin, and sildenafil as the compounds of major concern. Similarly, cumulative hazard per sample (ΣHQ) estimated high hazard towards aquatic organisms in every sample. All selected ARGs, except mcr-1 (polymyxin resistance), were detected. Among genes conferring resistance to beta-lactams, blaCTX-M and blaKPC were the most abundant, related to resistance to cephalosporins and carbapenems. Ecotoxicological evaluation showed mostly low toxicity towards Daphnia magna and Vibrio fischeri, contrary to the marked effect observed towards Lactuca sativa. These findings provide relevant and novel information on the risk posed by hospital wastewater and their pharmaceutical content in the Latin American environmental context.

Elucidating the Racemization Mechanism of Aliphatic and Aromatic Amino Acids by In Silico Tools.

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

Kinetic study of the catalytic cracking of waste motor oil using biomass-derived heterogeneous catalysts.

Herein, biochar from biomass residues is demonstrated as active materials for the catalytic cracking of waste motor oil into diesel-like fuels. Above all, alkali-treated rice husk biochar showed great activity with a 250% increase in the kinetic constant compared to the thermal cracking. It also showed better activity than synthetic materials, as previously reported. Moreover, much lower activation energy (185.77to293.48kJmol) for the cracking process was also obtained. According to materials characterization, the catalytic activity was more related to the nature of the biochar's surface than its specific surface area. Finally, liquid products complied with all the physical properties defined by international standards for diesel-like fuels, with the presence of hydrocarbons chains between C10-C27 similar to the ones obtained in commercial diesel.

Stochastic Nature and Intramolecular Coupling in Optical Response Profiles: Critical Analysis through Semiclassical Models.

We have studied the nonlinear absorptive and dispersive responses considering a molecular system consisting of two-levels, where aspects of the vibrational internal structure and intramolecular coupling are inserted, in addition to the considerations of interaction with the thermal reservoir. The Born-Oppenheimer electronic energy curve for this molecular model consists of two-intercrossing harmonic oscillator potentials with minima displaced in energy and nuclear coordinate. The results obtained show how these optical responses are sensitive to explicit considerations of both intramolecular coupling and the presence of the solvent through their stochastic interaction. Our study shows that the permanent dipoles of the system and the transition dipoles induced by electromagnetic field effects represent critical quantities for the analysis. The solvent action in our model is treated through the natural Bohr frequency shift to a time-dependent function, with explicit manifestations in its comparison as if the upper state were broadened. Significant variations in the nonlinear optical properties for cases of perturbative and saturative treatments, relaxation times, and optical propagation, mainly due to changes in the probe and pump intensities, are studied. Our studies relating the intramolecular effects with those generated by the presence of the solvent and its stochastic interaction with the solute of study, have allowed not only to analyze the influence of these in the profile of the optical responses, but they could also provide some insights into the analysis and characterization of molecular systems through nonlinear optical properties.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

Ecotoxicity of pesticide formulations and their mixtures: the case of potato crops in Costa Rica.

Despite their environmental implications, ecotoxicological information regarding pesticide mixtures is relatively scarce. This study aimed to determine the ecotoxicity of individual pesticide formulations and their mixtures (insecticides and fungicides), which are applied during the production cycle of potato, according to agricultural practices from a Latin American region in Costa Rica. Two benchmark organisms were employed: Daphnia magna and Lactuca sativa. First, the evaluation of individual formulations (chlorothalonil, propineb, deltamethrin+imidacloprid, ziram, thiocyclam and chlorpyrifos) revealed differences between available EC50 for active ingredients (a.i.) and their respective formulations toward D. magna; on the contrary, no information could be retrieved from scientific literature for comparison in the case of L. sativa. In general, acute toxicity was higher toward D. magna than L. sativa. Moreover, interactions could not be determined on L. sativa, as the chlorothalonil formulation was not toxic at high levels and the concentration-response to propineb could not be fitted to obtain an IC50 value. The commercial formulation composed of deltamethrin+imidacloprid followed the concentration addition model (when compared with parameters retrieved from individual a.i.) and the other three mixtures evaluated (I: chlorothalonil-propineb-deltamethrin+imidacloprid; II: chlorothalonil-propineb-ziram-thiocyclam; III: chlorothalonil-propineb-chlorpyrifos) produced an antagonistic effect on D. magna, thus suggesting less acute toxicity than their individual components. Subsequent chronic studies showed that one of the most toxic mixtures (II) negatively affected D. magna reproduction at sublethal concentrations indicating that this mixture poses a risk to this species if these pesticides co-exist in freshwater systems. These findings provide useful data to better estimate the impact of real agricultural practices related to the use of agrochemicals.

Hydrogel for the Controlled Delivery of Bioactive Components from Extracts of Eupatorium glutinosum Lam. Leaves.

This research reported a hydrogel loaded with the ethanolic and methanolic extracts of Eupatorium glutinosum Lam. The E. glutinosum extracts were characterized by phytochemical screening, Fourier-transform infrared spectroscopy (FTIR), thin-layer chromatography (TLC), and UV/Vis profile identification. This research also evaluated the pharmacological activity of the extracts using antimicrobial, antioxidant, and anti-inflammatory assays prior to polymeric encapsulation. Results indicate that extracts inhibit the Escherichia colii DH5-α (Gram negative) growth; excellent antioxidant activity was evaluated by the ferric reducing power and total antioxidant activity assays, and extracts showed an anti-hemolytic effect. Moreover, the cotton and microcrystalline cellulose hydrogels demonstrate successful encapsulation based on characterization and kinetics studies such as FTIR, extract release, and swelling degree. Moreover, effective antibacterial activity was registered by the loaded hydrogel. The overall results encourage and show that Eupatorium glutinosum-loaded hydrogel may find a wide range of bandage and wound healing applications in the biomedical area.

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms.

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

Study of potential inhibition of the estrogen receptor α by cannabinoids using an in silico approach: Agonist vs antagonist mechanism.

Breast cancer is the main cancer type with more than 2.2 million cases in 2020, and is the principal cause of death in women; with 685000 deaths in 2020 worldwide. The estrogen receptor is involved at least in 70% of breast cancer diagnoses, and the agonist and antagonist properties of the drug in this receptor play a pivotal role in the control of this illness. This work evaluated the agonist and antagonist mechanisms of 30 cannabinoids by employing molecular docking and dynamic simulations. Compounds with docking scores < -8 kcal/mol were analyzed by molecular dynamic simulation at 300 ns, and relevant insights are given about the protein's structural changes, centered on the helicity in alpha-helices H3, H8, H11, and H12. Cannabicitran was the cannabinoid that presented the best relative binding-free energy (-34.96 kcal/mol), and based on rational modification, we found a new natural-based compound with relative binding-free energy (-44.83 kcal/mol) better than the controls hydroxytamoxifen and acolbifen. Structure modifications that could increase biological activity are suggested.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches.

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q2 CV) and external (Q2 EXT) validation-were found i.e., MLR1 (Q2 CV = 0.893, Q2 EXT = 0.897), RF1 (Q2 CV = 0.889, Q2 EXT = 0.907), and IBK1 (Q2 CV = 0.891, Q2 EXT = 0.907). An ensemble model was built by averaging the predicted pIC50 of the three models, obtaining a Q2 EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic ß-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure-activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha's test requirements and has the statistics parameters R2 = 0.843, Q2CV = 0.785, and Q2ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

Na⋯B bond in NaBH 3 - : An induced spin-polarized bond.

The nature of the Na⋯B bond, in the recently synthesized NaBH 3 - adduct, is analyzed on the light of the Na- propensity to polarize along the bond axis as a consequence of the electric field produced by the BH3 fragment. The observed induced polarization has two consequences: (i) the energetic stabilization of the Na- , and (ii) the split of its valence electrons into two opposite lobes along the bond axis. Additionally, an analysis of the electron localization is presented using the information content of the correlated conditional pair density that reveals a significant delocalization between one lobe of the polarized Na- anion and the BH3 fragment at the equilibrium distance. Our findings reported here complement previous works on this system.

QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis.

In this study, a series of novel quinolinone-based thiosemicarbazones were designed in silico and their activities tested in vitro against Mycobacterium tuberculosis (M. tuberculosis). Quantitative structure-activity relationship (QSAR) studies were performed using quinolinone and thiosemicarbazide as pharmacophoric nuclei; the best model showed statistical parameters of R2 = 0.83; F = 47.96; s = 0.31, and was validated by several different methods. The van der Waals volume, electron density, and electronegativity model results suggested a pivotal role in antituberculosis (anti-TB) activity. Subsequently, from this model a new series of quinolinone-thiosemicarbazone 11a-e was designed and docked against two tuberculosis protein targets: enoyl-acyl carrier protein reductase (InhA) and decaprenylphosphoryl-ß-D-ribose-2'-oxidase (DprE1). Molecular dynamics simulation over 200 ns showed a binding energy of -71.3 to -12.7 Kcal/mol, suggesting likely inhibition. In vitro antimycobacterial activity of quinolinone-thiosemicarbazone for 11a-e was evaluated against M. bovis, M. tuberculosis H37Rv, and six different strains of drug-resistant M. tuberculosis. All compounds exhibited good to excellent activity against all the families of M. tuberculosis. Several of the here synthesized compounds were more effective than the standard drugs (isoniazid, oxafloxacin), 11d and 11e being the most active products. The results suggest that these compounds may contribute as lead compounds in the research of new potential antimycobacterial agents.

Synthesis, In Vitro, and In Silico Analysis of the Antioxidative Activity of Dapsone Imine Derivatives.

Dapsone (DDS) is an antibacterial drug with well-known antioxidant properties. However, the antioxidant behavior of its derivatives has not been well explored. In the present work, the antioxidant activity of 10 dapsone derivatives 4-substituted was determined by an evaluation in two in vitro models (DPPH radical scavenging assay and ferric reducing antioxidant power). These imine derivatives 1-10 were obtained through condensation between DDS and the corresponding aromatic aldehydes 4-substuited. Three derivatives presented better results than DDS in the determination of DPPH (2, 9, and 10). Likewise, we have three compounds with better reducing activity than dapsone (4, 9, and 10). In order to be more insight, the redox process, a conceptual DFT analysis was carried out. Molecular descriptors such as electronic distribution, the total charge accepting/donating capacity (I/A), and the partial charge accepting/donating capacity (ω+/ω-) were calculated to analyze the relative donor-acceptor capacity through employing a donor acceptor map (DAM). The DFT calculation allowed us to establish a relationship between GAPHOMO-LUMO and DAM with the observed antioxidant effects. According to the results, we concluded that compounds 2 and 3 have the lowest Ra values, representing a good antioxidant behavior observed experimentally in DPPH radical capturing. On the other hand, derivatives 4, 9, and 10 display the best reducing capacity activity with the highest ω- and Rd values. Consequently, we propose these compounds as the best antireductants in our DDS imine derivative series.